Abstract
In recent years, Machine Learning (ML) has witnessed a great increase of storage capacity of computer systems and an enormous growth of available information to work with thanks to the WWW. This has raised an opportunity for new real life applications of ML methods and also new cutting-edge ML challenges like: tackle with massive databases, Distributed Learning and Privacy-preserving Classification. In this paper a new method capable of dealing with this three problems is presented. The method is based on Artificial Neural Networks with incremental learning and Genetic Algorithms. As supported by the experimental results, this method is able to fastly obtain an accurate model based on the information of distributed databases without exchanging any data during the training process, without degrading its classification accuracy when compared with other non-distributed classical ML methods. This makes the proposed method very efficient and adequate for Privacy-Preserving Learning applications.
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References
Fontenla-Romero, O., Alonso-Betanzos, A., Castillo, E., Guijarro-Berdiñas, B.: A global optimum approach for one-layer neural networks. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 1429–1449. Springer, Heidelberg (2002)
Castillo, E., Fontenla-Romero, O., Guijarro-Berdiñas, B., Alonso-Betanzos, A.: A global optimum approach for one-layer neural networks. Neural Computation 14(6), 1429–1449 (2002)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
PASCAL2: Pattern Analysis, Statistical Modelling and Computational Learning, http://www.pascal-network.org/
Pascal Large Scale Learning Challenge, http://largescale.first.fraunhofer.de/
Sharma, T., Silvescu, A., Andorf, C., Caragea, D., Honavar, V.: Learning from Distributed Data Sets, Department of Computer Science, Iova State University, Ames, IA (2004)
Caragea, D., Silvescu, A., Honavar, V.: Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. LNCS, pp. 547–559. Springer, Heidelberg (2001)
Mangasarian, O.L., Wild, E.W.: Privacy-Preserving Classification of Horizontally Partitioned Data Via Random Kernels, Technical Report 07-03, Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin (October 2007)
Mangasarian, O.L., Wild, E.W., Fung, G.M.: Privacy-Preserving Classification of Vertically Partitioned Data via Random Kernels, Technical Report 07-02, Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin (September 2007)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD on Management of Data, Dallas, TX, USA, pp. 439–450 (2000)
Cohen, I., Cozman, F.G., Sebe, N., Cirelo, M.C., Huang, T.S.: Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction. IEEE Transactions on Pattern analysis and Machine Intelligence, 1553–1567 (2004)
Xie, Z., Hsu, W., Li Lee, M.: Generalization of Classification Rules, Department of CIT, Fundan University. Shanghai, China and School of Computing, National University of Singapore (2003)
Fung, G.M., Mangasarian, O.L.: Proximal Support Vector Machine Classifiers. In: Proceedings KDD 2001: Knowledge Discovery and Data Mining, pp. 77–86 (2001)
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning, vol. 6, Aarhus University, Computer Science Department (1993)
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Guijarro-Berdiñas, B., Martínez-Rego, D., Fernández-Lorenzo, S. (2009). Privacy-Preserving Distributed Learning Based on Genetic Algorithms and Artificial Neural Networks. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_27
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DOI: https://doi.org/10.1007/978-3-642-02481-8_27
Publisher Name: Springer, Berlin, Heidelberg
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